Exploiting a Statistical Threshold for Efficiently Identifying Correlated Pairs

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. If there is many item in the association rule, much time is required. Xiong(2004) studies new method which is to compute the support of upper. They used support of upper to the $^{\theta}$. But $^{\theta}$ is subjective. In this paper, we present statistical objective criterion for efficiently identifying correlated pairs.